---
title: "Who cites Cosmos QA: Machine Reading Comprehension with…"
description: "The papers of the Elamur Research library that cite «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning», found in their…"
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# Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 3

266 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/cited-by/page/2/) · **3**

1. [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://aclanthology.org/2021.naacl-main.467/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
2. [Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI](https://aclanthology.org/2021.emnlp-main.303/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
3. [Discrete Reasoning Templates for Natural Language Understanding](https://aclanthology.org/2021.eacl-srw.12/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop*
4. [ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations](https://aclanthology.org/2021.emnlp-main.597/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
5. [English Machine Reading Comprehension Datasets: A Survey](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
6. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
7. [EventPlus: A Temporal Event Understanding Pipeline](https://aclanthology.org/2021.naacl-demos.7/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations*
8. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
9. [ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.609/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
10. [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://arxiv.org/abs/2104.09570) *— 2021 · arXiv:2104.09570*
11. [Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers](https://aclanthology.org/2021.blackboxnlp-1.42/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
12. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
13. [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://aclanthology.org/2021.acl-long.357/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
14. [Generating Hypothetical Events for Abductive Inference](https://aclanthology.org/2021.starsem-1.6/) *— 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
15. [Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks](https://aclanthology.org/2021.eacl-main.192/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
16. [It's Commonsense, isn't it? Demystifying Human Evaluations in Commonsense-Enhanced NLG Systems](https://aclanthology.org/2021.humeval-1.1/) *— 2021 · Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)*
17. [Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization](https://aclanthology.org/2021.naacl-main.104/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
19. [Modeling Context in Answer Sentence Selection Systems on a Latency Budget](https://aclanthology.org/2021.eacl-main.261/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
20. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
21. [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
22. [NLQuAD: A Non-Factoid Long Question Answering Data Set](https://aclanthology.org/2021.eacl-main.106/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
23. [On Commonsense Cues in BERT for Solving Commonsense Tasks](https://aclanthology.org/2021.findings-acl.61/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
24. [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) *— 2021 · arXiv:2107.13586*
25. [RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms](https://aclanthology.org/2021.emnlp-main.598/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
26. [Syntax-Enhanced Pre-trained Model](https://aclanthology.org/2021.acl-long.420/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
27. [TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models](https://aclanthology.org/2021.acl-long.469/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
28. [Towards a Language Model for Temporal Commonsense Reasoning](https://aclanthology.org/2021.ranlp-srw.12/) *— 2021 · Proceedings of the Student Research Workshop Associated with RANLP 2021*
29. [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://arxiv.org/abs/2103.13009) *— 2021 · arXiv:2103.13009*
30. [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://aclanthology.org/2021.acl-long.98/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
31. [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
32. [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
33. [A Benchmark Arabic Dataset for Commonsense Explanation](https://arxiv.org/abs/2012.10251) *— 2020 · arXiv:2012.10251*
34. [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://arxiv.org/abs/2005.00247) *— 2020 · arXiv:2005.00247 · Proceedings of EACL 2021*
35. [Adversarial Training for Commonsense Inference](https://aclanthology.org/2020.repl4nlp-1.8/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
36. [Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning](https://aclanthology.org/2020.emnlp-main.58/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
37. [Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models](https://arxiv.org/abs/2005.14709) *— 2020 · arXiv:2005.14709*
38. [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://aclanthology.org/2020.findings-emnlp.165/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
39. [Commonsense Evidence Generation and Injection in Reading Comprehension](https://aclanthology.org/2020.sigdial-1.9/) *— 2020 · Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
40. [Commonsense Reasoning for Natural Language Processing](https://aclanthology.org/2020.acl-tutorials.7/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts*
41. [Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering](https://aclanthology.org/2020.findings-emnlp.369/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
42. [Conversational Machine Comprehension: a Literature Review](https://aclanthology.org/2020.coling-main.247/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
43. [Designing Precise and Robust Dialogue Response Evaluators](https://aclanthology.org/2020.acl-main.4/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
44. [English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://aclanthology.org/2020.aacl-main.56/) *— 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*
45. [Improving Event Detection via Open-domain Trigger Knowledge](https://aclanthology.org/2020.acl-main.522/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
46. [Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks](https://aclanthology.org/2020.deelio-1.9/) *— 2020 · Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures*
47. [Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
48. [Is this sentence valid? An Arabic Dataset for Commonsense Validation](https://arxiv.org/abs/2008.10873) *— 2020 · arXiv:2008.10873*
49. [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://arxiv.org/abs/2002.01808) *— 2020 · arXiv:2002.01808*
50. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
51. [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) *— 2020 · arXiv:2009.03300*
52. [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://aclanthology.org/2020.acl-main.130/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
53. [Paragraph-level Commonsense Transformers with Recurrent Memory](https://arxiv.org/abs/2010.01486) *— 2020 · arXiv:2010.01486*
54. [ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning](https://aclanthology.org/2020.emnlp-main.85/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
55. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
56. [Scene Restoring for Narrative Machine Reading Comprehension](https://aclanthology.org/2020.emnlp-main.247/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
57. [Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension](https://aclanthology.org/2020.lantern-1.3/) *— 2020 · Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)*
58. [TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation](https://aclanthology.org/2020.semeval-1.76/) *— 2020 · Proceedings of the Fourteenth Workshop on Semantic Evaluation*
59. [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.327/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
60. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
61. [Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text](https://aclanthology.org/2020.emnlp-main.111/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
62. [Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning](https://aclanthology.org/2020.findings-emnlp.289/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
63. [Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty](https://aclanthology.org/2020.nlpcss-1.13/) *— 2020 · Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science*
64. [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://aclanthology.org/2020.emnlp-main.185/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
65. [On Making Reading Comprehension More Comprehensive](https://aclanthology.org/D19-5815/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
66. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*

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